Search Results - "Leistner, C"

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  1. 1

    Modeling and simulation of the curing process of epoxy resins using finite elements by Leistner, C., Hartmann, S., Abliz, D., Ziegmann, G.

    Published in Continuum mechanics and thermodynamics (01-03-2020)
    “…This article discusses several aspects of the curing process in polymers. First, we collect experimental data for an Araldite epoxy resin and calibrate the…”
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    Journal Article
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    Tick-borne meningoencephalitis in a 4.5-month-old infant by Leistner, C, Dahlem, P

    Published in Klinische Padiatrie (01-07-2011)
    “…The course of tick-borne encephalitis (TBE) in children is supposed to be mild though severe neurological sequelae have been reported. Only occasionally…”
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    Journal Article
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    Recycling of solution spaces in multipreconditioned FETI methods applied to structural dynamics by Leistner, Michael C., Gosselet, Pierre, Rixen, Daniel J.

    “…Summary This article presents a new method to recycle the solution space of an adaptive multipreconditioned finite element tearing and interconnecting…”
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    Journal Article
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    Semantic tie points by Montoya-Zegarra, J. A., Leistner, C., Schindler, K.

    “…Images for 3D mapping are always recorded in such a way that relevant scene parts are seen from multiple viewpoints, so as to facilitate camera orientation and…”
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    Conference Proceeding Journal Article
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    Learning object class detectors from weakly annotated video by Prest, Alessandro, Leistner, C., Civera, J., Schmid, C., Ferrari, V.

    “…Object detectors are typically trained on a large set of still images annotated by bounding-boxes. This paper introduces an approach for learning object…”
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    Conference Proceeding
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    PROST: Parallel robust online simple tracking by Santner, J, Leistner, C, Saffari, A, Pock, T, Bischof, H

    “…Tracking-by-detection is increasingly popular in order to tackle the visual tracking problem. Existing adaptive methods suffer from the drifting problem, since…”
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    Conference Proceeding
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    Interactive object detection by Yao, A., Gall, J., Leistner, C., Van Gool, L.

    “…In recent years, the rise of digital image and video data available has led to an increasing demand for image annotation. In this paper, we propose an…”
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    Conference Proceeding
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    On-line semi-supervised multiple-instance boosting by Zeisl, B, Leistner, C, Saffari, A, Bischof, H

    “…A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these…”
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    Conference Proceeding
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    Audio-Visual Co-Training for Vehicle Classification by Godec, M, Leistner, C, Bischof, H, Starzacher, A, Rinner, B

    “…In this paper, we introduce a fully autonomous vehicle classification system that continuously learns from largeamounts of unlabeled data. For that purpose, we…”
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    Conference Proceeding
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    Online multi-class LPBoost by Saffari, A, Godec, M, Pock, T, Leistner, C, Bischof, H

    “…Online boosting is one of the most successful online learning algorithms in computer vision. While many challenging online learning problems are inherently…”
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    Conference Proceeding
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    Autonomous Audio-Supported Learning of Visual Classifiers for Traffic Monitoring by Bischof, Horst, Godec, Martin, Leistner, Christian, Rinner, Bernhard, Starzacher, Andreas

    Published in IEEE intelligent systems (01-05-2010)
    “…In this paper, using acoustic detection and classification of vehicles, the proposed autonomous self-learning framework generates scene adaptive vehicle…”
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    Journal Article
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    Recycling of Solution Spaces in Multi-Preconditioned FETI Methods Applied to Structural Dynamics by Leistner, Michael C., Gosselet, Pierre, Rixen, Daniel J.

    “…This article presents a new method to recycle the solution space of an adaptive multi-preconditioned finite element tearing and interconnecting (AMP-FETI)…”
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    Journal Article
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    On-Line Random Naive Bayes for Tracking by Godec, M, Leistner, C, Saffari, A, Bischof, H

    “…Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computer vision applications. However, it was shown that the…”
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    Conference Proceeding
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    Semi-supervised boosting using visual similarity learning by Leistner, C., Grabner, H., Bischof, H.

    “…The required amount of labeled training data for object detection and classification is a major drawback of current methods. Combining labeled and unlabeled…”
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    Conference Proceeding
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    Improving classifiers with unlabeled weakly-related videos by Leistner, C., Godec, M., Schulter, S., Saffari, A., Werlberger, M., Bischof, H.

    Published in CVPR 2011 (01-06-2011)
    “…Current state-of-the-art object classification systems are trained using large amounts of hand-labeled images. In this paper, we present an approach that shows…”
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    Conference Proceeding
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